Method of deep learining-based examination of a semiconductor specimen and system thereof

ABSTRACT

There are provided system and method of examining a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. The examination-related application can be, for example, classifying at least one defect presented by at least one FP image, segmenting the at least one FP image, detecting defects in the specimen presented by the at least one FP image, registering between at least two FP images, regression application enabling reconstructing the at least one FP image in correspondence with different examination modality, etc.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims benefit from 62/271,219 filed on Dec. 22,2015 and is incorporated hereby by reference in its entirety.

TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the fieldof examination of a specimen, and more specifically, to methods andsystems for automating of a specimen's examination.

BACKGROUND

Current demands for high density and performance associated with ultralarge scale integration of fabricated devices require submicronfeatures, increased transistor and circuit speeds, and improvedreliability. Such demands require formation of device features with highprecision and uniformity, which, in turn, necessitates carefulmonitoring of the fabrication process, including frequent and detailedinspections of the devices while they are still in the form ofsemiconductor wafers.

The term “specimen” used in this specification should be expansivelyconstrued to cover any kind of wafer, masks, and other structures,combinations and/or parts thereof used for manufacturing semiconductorintegrated circuits, magnetic heads, flat panel displays, and othersemiconductor-fabricated articles.

The term “examination” used in this specification should be expansivelyconstrued to cover any kind of metrology-related operations as well asoperations related to detection and/or classification of defects in aspecimen during its fabrication. Examination is provided by usingnon-destructive examination tools during or after manufacture of thespecimen to be examined. By way of non-limiting example, the examinationprocess can include runtime scanning (in a single or in multiple scans),sampling, reviewing, measuring, classifying and/or other operationsprovided with regard to the specimen or parts thereof using the same ordifferent inspection tools. Likewise, examination can be provided priorto manufacture of the specimen to be examined and can include, forexample, generating an examination recipe(s) and/or other setupoperations. It is noted that, unless specifically stated otherwise, theterm “examination” or its derivatives used in this specification are notlimited with respect to resolution or size of an inspection area. Avariety of non-destructive examination tools includes, by way ofnon-limiting example, scanning electron microscopes, atomic forcemicroscopes, optical inspection tools, etc.

By way of non-limiting example, run-time examination can employ a twophase procedure, e.g. inspection of a specimen followed by review ofsampled defects. During the first phase, the surface of a specimen isinspected at high-speed and relatively low-resolution. In the firstphase, a defect map is produced to show suspected locations on thespecimen having high probability of a defect. During the second phasethe suspected locations are more thoroughly analyzed with relativelyhigh resolution. In some cases both phases can be implemented by thesame inspection tool, and, in some other cases, these two phases areimplemented by different inspection tools.

Examination processes are used at various steps during semiconductorfabrication to detect and classify defects on specimens. Effectivenessof examination can be increased by automatization of process(es) as, forexample, Automatic Defect Classification (ADC), Automatic Defect Review(ADR), etc.

GENERAL DESCRIPTION

In accordance with certain aspect of the presently disclosed subjectmatter, there is provided a method of examination of a semiconductorspecimen, the method comprising: upon obtaining by a computer a DeepNeural Network (DNN) trained for a given examination-related applicationwithin a semiconductor fabrication process, processing together one ormore fabrication process (FP) images using the obtained trained DNN,wherein the DNN is trained using a training set comprising ground truthdata specific for the given application; and obtaining by the computerexamination-related data specific for the given application andcharacterizing at least one of the processed one or more FP images.

The examination-related application can be, for example, classifying atleast one defect presented by the at least one FP image, segmenting theat least one FP image, detecting defects in the specimen presented bythe at least one FP image, registering between at least two FP images,and regression application enabling reconstructing the at least one FPimage in correspondence with different examination modality, etc.

In accordance with further aspects of the presently disclosed subjectmatter, the training set can comprise a plurality of first trainingsamples and a plurality of augmented training samples obtained byaugmenting at least part of the first training samples. The training setcan further comprise ground truth data associated with the firsttraining samples and augmented ground truth data associated with theaugmented training samples. Optionally, a number of augmented trainingsamples can be substantially larger than a number of first trainingsamples. Optionally, at least substantial part of augmented trainingsamples can be obtained by one or more augmenting techniques preferableto the given application.

In accordance with further aspects of the presently disclosed subjectmatter, each first training sample can comprise at least one imageobtained by an examination modality such as optical inspection;multi-perspective optical inspection, low-resolution inspection byelectronic microscope, high-resolution inspection by electronicmicroscope, image generation based on design data, image generation byaltering a captured image, etc.

The one or more FP images can constitute a FP sample, wherein each firsttraining sample comprises images obtained from the same modalities asthe one or more FP images. Optionally, each first training sample canfurther comprises at least one image obtained by an examination modalityother than one or more examination modalities used for obtaining the oneor more FP images.

In accordance with further aspects of the presently disclosed subjectmatter, the given examination-related application can be related to acertain production layer. In such a case, respective training setcomprises ground truth data specific for said certain production layerand the examination-related data is specific for said certain productionlayer.

In accordance with further aspects of the presently disclosed subjectmatter, the given examination-related application can be related to acertain virtual layer consisting of one or more production layers with asimilar nature. In such a case, respective training set comprises groundtruth data specific for said certain virtual layer and theexamination-related data is specific for said certain virtual layer.

In accordance with further aspects of the presently disclosed subjectmatter, examination flow can comprise at least a firstexamination-related application and a second examination-relatedapplication. In such a case, the method further comprises using for thefirst application a DNN trained using a training set comprising groundtruth data specific for the first application and using for the secondapplication a DNN trained using a training set comprising ground truthdata specific for the second application.

In accordance with further aspects of the presently disclosed subjectmatter, the given examination-related application can be classifying atleast one defect presented by the at least one FP image. In such a case,the ground truth data can be informative of classes and/or of classdistribution of defects presented in the first training samples andaugmented ground truth data can be informative of classes and/or ofclass distribution of defects presented in the augmented trainingsamples. Augmenting at least part of the first training samples can beprovided, for example, by geometrical warping, planting a new defect inan image, amplifying a defectiveness of a pre-existing defect in animage, removing a pre-existing defect from an image and disguising adefect in an image.

In accordance with further aspects of the presently disclosed subjectmatter, the given examination-related application can be segmenting theat least one FP image (e.g. a high-resolution image of the specimen, alow-resolution image of the specimen or a design data-based image of thespecimen). In such a case, examination-related data can be informativeof per-pixel segmentation-related values of the at least one FP image.

In accordance with further aspects of the presently disclosed subjectmatter, the given examination-related application can be detectingdefects in the specimen. In such a case, the examination-related datacan be informative of true defects presented in the at least one FPimage.

In accordance with further aspects of the presently disclosed subjectmatter, the given examination-related application can be registrationbetween at least two FP images. In such a case, training set comprises aplurality of training samples each comprising at least a pair of imagesregisterable one with regard to another, and the examination-relateddata is informative of registration-related data with regard to said atleast two FP images.

In accordance with further aspects of the presently disclosed subjectmatter, the given examination-related application can be a regressionapplication enabling reconstructing the at least one FP image incorrespondence with different examination modality. In such a case, thetraining set can comprise a plurality of training samples with imagesobtained by first examination modalities, each said training sampleassociated with ground truth data comprising one or more correspondingimages obtained by one or more another examination modalities.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a computer, cause thecomputer to perform a method of examination of a semiconductor specimenas disclosed above.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a system usable for examination of asemiconductor specimen, in accordance with the aspects disclosed above.The system can comprise a processing and memory block (PMB) operativelyconnected to an input interface and an output interface, wherein: theinput interface is configured to receive one or more fabrication process(FP) images; the PMB is configured to obtain a Deep Neural Network (DNN)trained for a given examination-related application within asemiconductor fabrication process and to process together the one ormore received FP images using the trained DNN to obtainexamination-related data specific for the given application andcharacterizing at least one of the processed one or more FP images,wherein the DNN is trained using a training set comprising ground truthdata specific for the given application; the output interface isconfigured to output the obtained examination-related data. The outputexamination-related data can be usable by one or more examination toolsinvolved in the examination of the specimen.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the disclosure and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 illustrates a functional block diagram of an examination systemin accordance with certain embodiments of the presently disclosedsubject matter;

FIG. 2 illustrates a generalized model of an exemplified deep neuralnetwork usable in accordance with certain embodiments of the presentlydisclosed subject matter;

FIG. 3 illustrates a generalized flow-chart of automatically determiningexamination-related data using fabrication process (FP) images inaccordance with certain embodiments of the presently disclosed subjectmatter;

FIG. 4 illustrates a generalized flow-chart of training a deep neuralnetwork (DNN) in accordance with certain embodiments of the presentlydisclosed subject matter;

FIG. 5a and FIG. 5b illustrate generalized flow-charts of classifyingdefects in accordance with certain embodiments of the presentlydisclosed subject matter;

FIG. 6 illustrates a generalized flow-chart of segmentation ofexamination-related images in accordance with certain embodiments of thepresently disclosed subject matter;

FIG. 7 illustrates a generalized flow-chart of defect detection inaccordance with certain embodiments of the presently disclosed subjectmatter;

FIG. 8 illustrates a generalized flow-chart of registrationexamination-related images in accordance with certain embodiments of thepresently disclosed subject matter; and

FIG. 9 illustrates a generalized flow-chart of cross-modality regressionin accordance with certain embodiments of the presently disclosedsubject matter.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the disclosure.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “computing”,“representing”, “comparing”, “generating”, “training”, “segmenting”,“registering” or the like, refer to the action(s) and/or process(es) ofa computer that manipulate and/or transform data into other data, saiddata represented as physical, such as electronic, quantities and/or saiddata representing the physical objects. The term “computer” should beexpansively construed to cover any kind of hardware-based electronicdevice with data processing capabilities including, by way ofnon-limiting example, FPEI system and parts thereof disclosed in thepresent application.

The terms “non-transitory memory” and “non-transitory storage medium”used herein should be expansively construed to cover any volatile ornon-volatile computer memory suitable to the presently disclosed subjectmatter.

The term “defect” used in this specification should be expansivelyconstrued to cover any kind of abnormality or undesirable feature formedon or within a specimen.

The term “design data” used in the specification should be expansivelyconstrued to cover any data indicative of hierarchical physical design(layout) of a specimen. Design data can be provided by a respectivedesigner and/or can be derived from the physical design (e.g. throughcomplex simulation, simple geometric and Boolean operations, etc.).Design data can be provided in different formats as, by way ofnon-limiting examples, GDSII format, OASIS format, etc. Design data canbe presented in vector format, grayscale intensity image format orotherwise.

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are describedin the context of separate embodiments, can also be provided incombination in a single embodiment. Conversely, various features of thepresently disclosed subject matter, which are described in the contextof a single embodiment, can also be provided separately or in anysuitable sub-combination. In the following detailed description,numerous specific details are set forth in order to provide a thoroughunderstanding of the methods and apparatus.

Bearing this in mind, attention is drawn to FIG. 1 illustrating afunctional block diagram of an examination system in accordance withcertain embodiments of the presently disclosed subject matter. Theexamination system 100 illustrated in FIG. 1 can be used for examinationof a specimen (e.g. of a wafer and/or parts thereof) as a part ofspecimen fabrication. The illustrated examination system 100 comprisescomputer-based system 103 capable of automatically determiningmetrology-related and/or defect-related information using imagesobtained in specimen fabrication (referred to hereinafter as fabricationprocess (FP) images). The system 103 is referred to hereinafter as anFPEI (Fabrication Process Examination Information) system. FPEI system103 can be operatively connected to one or more low-resolutionexamination tools 101 and/or one or more high-resolution examinationtools 102. The examination tools are configured to capture inspectionimages and/or to review the captured inspection image(s) and/or toenable or provide measurements related to the captured image(s). FPEIsystem is further operatively connected to CAD server 110 and datarepository 109.

FPEI system 103 comprises a processor and memory block (PMB) 104operatively connected to a hardware-based input interface 105 and to ahardware-based output interface 106. PMB 104 is configured to provideall processing necessary for operating FPEI system further detailed withreference to FIGS. 2-9 and comprises a processor (not shown separately)and a memory (not shown separately). The processor of PMB 104 can beconfigured to execute several functional modules in accordance withcomputer-readable instructions implemented on a non-transitorycomputer-readable memory comprised in PMB. Such functional modules arereferred to hereinafter as comprised in the PMB. Functional modulescomprised in the processor include operatively connected training setgenerator 111 and Deep Neural Network (DNN) 112. DNN 112 comprises a DNNmodule 114 configured to enable data processing using deep neuralnetwork(s) for outputting application-specific data (e.g.classification, detection, regression, etc.) based on the input data.Optionally, DNN 112 can comprise pre-DNN module 113 configured toprovide preprocessing before forwarding data to DNN module and/orpost-DNN module 115 configured to provide post-processing data generatedby DNN module. Operation of FPEI system 103, PMB 104 and the functionalblocks therein will be further detailed with reference to FIGS. 2-9.

As will be further detailed with reference to FIGS. 2-9, FPEI system isconfigured to receive, via input interface 105, data (and/or derivativesthereof) produced by the examination tools and/or data stored in one ormore data repositories 109 and/or in CAD server 110 and/or anotherrelevant data depository. FPEI system is further configured to processthe received data and send, via output interface 106, the results (orpart thereof) to a storage system 107, to examination tool(s), to acomputer-based graphical user interface (GUI) 108 for rendering theresults and/or to external systems (e.g. Yield Management System (YMS)of a FAB). GUI 108 can be further configured to enable user-specifiedinputs related to operating FPEI system 103.

By way of non-limiting example, a specimen can be examined by alow-resolution examination machine 101 (e.g. an optical inspectionsystem, low-resolution SEM, etc.). The resulting data (referred tohereinafter as low-resolution image data 121) informative oflow-resolution images (and/or derivatives thereof) can betransmitted—directly or via one or more intermediate systems—to FPEIsystem 103. Alternatively or additionally, the specimen can be examinedby a high-resolution machine 102 (e.g. a subset of potential defectlocations selected for review can be reviewed by a scanning electronmicroscope (SEM) or Atomic Force Microscopy (AFM)). The resulting data(referred to hereinafter as high-resolution image data 122) informativeof high-resolution images and/or derivatives thereof can be transmitted—directly or via one or more intermediate systems—to FPEI system 103.

Upon processing the received image data (optionally together with otherdata as, for example, design data) FPEI system can send the results(e.g. instruction-related data 123 and/or 124) to any of the examinationtool(s), store the results (e.g. defect classification) in storagesystem 107, render the results via GUI 108 and/or send to an externalsystem (e.g. to YMS).

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not bound by the systemillustrated in FIG. 1; equivalent and/or modified functionality can beconsolidated or divided in another manner and can be implemented in anyappropriate combination of software with firmware and hardware.

It is noted that the examination system illustrated in FIG. 1 can beimplemented in a distributed computing environment, in which theaforementioned functional modules shown in FIG. 1 can be distributedover several local and/or remote devices, and can be linked through acommunication network. It is further noted that in another embodimentsat least part of examination tools 101 and/or 102, data repositories109, storage system 107 and/or GUI 108 can be external to theexamination system 100 and operate in data communication with FPEIsystem 103 via input interface 105 and output interface 106. FPEI system103 can be implemented as stand-alone computer(s) to be used inconjunction with the examination tools. Alternatively, the respectivefunctions of FPEI system can, at least partly, be integrated with one ormore examination tools.

Without limiting the scope of the disclosure in any way, it should alsobe noted that the examination tools can be implemented as inspectionmachines of various types, such as optical imaging machines, electronbeam inspection machines and so on. In some cases the examination toolscan be configured to examine an entire specimen (e.g. an entire wafer orat least an entire die) for detection of potential defects. In othercases, at least one examination tool can be a review tool, which istypically of higher resolution and which is used for ascertainingwhether a potential defect is indeed a defect. Such a review tool isusually configured to inspect fragments of a die, one at a time, inhigher resolution. In some cases at least one examination tool can havemetrology capabilities.

A generalized model of an exemplified deep neural network usable as DNN112 is illustrated in FIG. 2. The illustrated exemplified DNN comprisesDNN module 114 with input layer 201, output layer 203 and one or morehidden layers (denoted as 202-1, 202-2 and 202-i) disposed between theinput layer and the output layer. Optionally, DNN comprises pre-DNNmodule 113 and post-DNN module 114.

Each layer of DNN module 114 can include multiple basic computationalelements (CE) 204 typically referred to in the art as dimensions,neurons, or nodes. CEs comprised in the input layer are denoted in FIG.2 by letter “i”, CEs comprised in the hidden layers are denoted byletter “h”, and CEs comprised in the output layer are denoted by letter“o”. Computational elements of a given layer are connected with CEs of asubsequent layer by connections 205. Each connection 205 between CE ofpreceding layer and CE of subsequent layer is associated with aweighting value (for simplicity, not shown in FIG. 2).

A given hidden CE can receive inputs from CEs of a previous layer viathe respective connections, each given connection being associated witha weighting value which can be applied to the input of the givenconnection. The weighting values can determine the relative strength ofthe connections and thus the relative influence of the respective inputson the output of the given CE. The given hidden CE can be configured tocompute an activation value (e.g. the weighted sum of the inputs) andfurther derive an output by applying an activation function to thecomputed activation. The activation function can be, for example, anidentity function, a deterministic function (e.g., linear, sigmoid,threshold, or the like), a stochastic function or other suitablefunction. The output from the given hidden CE can be transmitted to CEsof a subsequent layer via the respective connections. Likewise, asabove, each connection at the output of a CE can be associated with aweighting value which can be applied to the output of the CE prior tobeing received as an input of a CE of a subsequent layer. Further to theweighting values, there can be threshold values (including limitingfunctions) associated with the connections and CEs.

The weighting and/or threshold values of a deep neural network can beinitially selected prior to training, and can be further iterativelyadjusted or modified during training to achieve an optimal set ofweighting and/or threshold values in the trained DNN module. After eachiteration, a difference can be determined between the actual outputproduced by DNN module and the target output associated with therespective training set of data. The difference can be referred to as anerror value. Training can be determined to be complete when a costfunction indicative of the error value is less than a predeterminedvalue or when a limited change in performance between iterations isachieved.

A set of DNN input data used to adjust the weights/thresholds of thedeep neural network is referred to hereinafter as a training set.

Inputs to DNN 112 can be pre-processed by pre-DNN module 113 prior toinputting to DNN module 114, and/or outputs of DNN module 114 can bepost-processed by post-DNN module 115 before outputting from DNN 112. Insuch cases training of DNN 112 further includes determining parametersof pre-DNN module and/or post-DNN module. DNN module can be trained soas to minimize cost function of the entire DNN, while parameters ofpre-DNN module and/or post-DNN module can be predefined and, optionally,can be adjusted during the training. A set of training-based parameterscan further include parameters related to pre-DNN and post-DNNprocessing.

It is noted that the teachings of the presently disclosed subject matterare not bound by the number of hidden layers and/or by DNN architecture.By way of non-limiting example, the layers in DNN can be convolutional,fully connected, locally connected, pooling/subsampling, recurrent, etc.

Referring to FIG. 3, there is illustrated a generalized flow-chart ofautomatically determining examination-related data using fabricationprocess (FP) images. In accordance with certain embodiments of thepresently disclosed subject matter, the method comprises a setup stepcomprising training the Deep Neural Network (DNN) 112, wherein DNN istrained for a given examination-related application and is characterizedby an application-specific set of training-based parameters. TrainingDNN 112 in accordance with certain embodiments of the presentlydisclosed subject matter is further detailed with reference to FIG. 4.By way of non-limiting example, examination-related application can beone of the following:

-   -   defect classification using attributes generated by DNN        (defining classes can include modifying and/or updating        preexisting class definitions);    -   segmentation of the fabrication process image including        partitioning of FP image into segments (e.g. material types,        edges, pixel labeling,    -   regions of interest, etc.);    -   defect detection (e.g. identifying one or more candidate defects        (if they exist) using FP image and marking thereof, determining        truth value for candidate defects, obtaining shape information        for the defects, etc.).    -   registration between two or more images including obtaining the        geometrical warping parameters between the images (can be global        or local, simple as shift or more complex transformations);    -   cross-modality regression (e.g. reconstructing an image from one        or more images from a different examination modality as, for        example, SEM or optical image from CAD, height map from SEM        images, high resolution image from low resolution images);    -   combination(s) of the above.

Upon obtaining (301) the DNN trained for a given application during thesetup step, the PMB of FPEI system, during the runtime, processes (302)together one or more FP images using the obtained trained DNN, andobtains (303) application-specific examination-related datacharacterizing at least one of the processed one or more FP images. Whenprocessing one or more FP images, PMB can also use predefined parametersand/or parameters received from other sources in addition to thetraining-based parameters characterizing DNN.

FP images to be processed together by the trained DNN can arrive fromdifferent examination modalities (e.g. from different examination tools;from different channels of the same examination tool as, for example,bright field and dark field images; from the same examination tool usingdifferent operational parameters, can be derived from design data, etc.)

FP images can be selected from images of specimen (e.g. wafer or partsthereof) captured during the fabrication process, derivatives of thecapture images obtained by various pre-processing stages (e.g. images ofa part of a wafer or a photomask captured by SEM or an opticalinspection system, SEM images roughly centered around the defect to beclassified by ADC, SEM images of larger regions in which the defect isto be localized by ADR, registered images of different examinationmodalities corresponding to the same mask location, segmented images,height map images, etc.) and computer-generated design data-basedimages.

By way of non-limiting example, application-specific examination-relateddata can represent a per-pixel map of values whose meaning depends on anapplication (e.g. binary map for defect detection; discrete map fornuisance family prediction indicating the family type or general class;discrete map for defect type classification; continuous values for crossmodality or die-to model (D2M) regression, etc.). Per-pixel map can befurther obtained together with per-pixel probability map indicative ofprobability of values obtained for the pixels.

Alternatively or additionally, application-specific examination-relateddata can represent one or more values summarizing the entire imagecontent (not per-pixel), such as, for example, defect bounding boxcandidates and associated defectiveness probabilities for automaticdefect review application, defect class and class probability forautomatic defect classification application, etc.

Alternatively or additionally, obtained application-specificdefect-related data can be not directly related to defects, but beusable for defect analyses (e.g. boundaries between layers of the waferobtained by segmentation of FP images can be usable for definingdefects' layers, defect environment data as, for example,characteristics of the background pattern, etc.). Alternatively oradditionally, examination-related data can be usable for metrologypurposes.

Non-limiting examples of application-specific FP images (DNN input) andapplication-specific examination-related data (DNN output) areillustrated in Table 1.

TABLE 1 Application-specific FP images and application-specificexamination- related data Non-limiting example of application-specificNon-limiting example of examination-related Applicationapplication-specific FP images data Classification Defect images,reference die Defect classification, images, height map, CAD attributes(e.g. to be used images, defect mask in other classifiers) RegressionCAD image Optical or SEM image Regression SEM images (including Heightmap perspectives) Regression Low resolution images High resolution imageRegression Noisy images De-noised image Segmentation Optical or SEMimages, with Segmentation map (label or without CAD per pixel). DefectDefect image (e.g. optical or Defect bounding box detection SEM,reference image or coordinate, defect mask images (optional), image (alldefect pixels CAD (optional). are “1”, others “0”), etc. RegistrationTwo images from the same Registration parameters examination modality(e.g. (for a parametric Optical or SEM). module) Registration Two imagesfrom different Optical flow map (X and examination modalities (SEM- Ydisplacements for each Optical, Optical-CAD, SEM- pixel) CAD)

Non-limiting examples of processing FP images and obtainingapplication-specific examination-related data are further detailed withreference to FIGS. 5-9. The technique illustrated with reference toFIGS. 3-4 is applicable for mask examination and/or metrology flow andwafer examination and/or metrology flow (e.g. D2D, SD and CAD-aided,ADR/ADC flows, etc.), for multi-modality and single image flows such asCAD-2-SEM registration, for multi-perspective detection (ADR), formulti-perspective classification (ADC), etc.

It is noted that a given examination-related application can be furthercharacterized by a certain production layer to be examined or a groupthereof. By way of non-limiting example, defect detection and/orclassification for a “virtual layer” constituted by one or more metallayers can use attributes generated by DNN specially trained for thisvirtual layer. Likewise, another specially trained DNN can be used fordefect detection and/or classification in a “virtual layer” constitutedby one or more mask layers.

Referring to FIG. 4, there is illustrated a generalized flow-chart oftraining DNN 112 in accordance with certain embodiments of the presentlydisclosed subject matter. When used in conjunction with obtainingapplication-specific information, DNN 112 is trained for a givenexamination-related application and is characterized byapplication-specific training-based parameters.

When training DNN 112, FPEI system obtains (401) a set of first trainingsamples, obtains (402) first ground truth data corresponding to thefirst training samples and processes the first training samples andfirst ground truth data to generate (e.g. by the training set generator)(403) an application-specific training set.

The set of first training samples and ground truth data can be obtainedvia input interface 105 from data repository 109, CAD server 110 or anyother suitable data repository. Alternatively or additionally, groundtruth data can be obtained via GUI 108.

Depending on application, a training sample can be a single image or agroup of images of specimen obtained by the same or by differentexamination modalities. It is noted that examination modalities candiffer one from another by sources of respective images (e.g. imagescaptured by a scanning electron microscope (SEM), by images captured byan optical inspection system, images derived from the captured images,CAD-based generated images, etc.) and/or by deriving techniques appliedto the captured images (e.g. images derived by segmentation, defectcontour extraction, height map calculation, etc.) and/or by examinationparameters (e.g. by perspective and/or resolution provided by a certainexamination tool, etc.). It is further noted that all first trainingsamples used for a given training process shall be constituted by thesame number of images obtained by the same examination modalities andhaving the same relationship within the training sample (e.g. singleimage from a certain modality, or a pair constituted by an image and areference image, or a group constituted by a top perspective image, 4side perspective images and a CAD-based image, etc.)

Values of ground truth data include images and/or labels associated withapplication-specific training samples. Ground truth data can besynthetically produced (e.g. CAD-based images), actually produced (e.g.captured images), produced by machine-learning annotation (e.g. labelsbased on feature extracting and analysis); produced by human annotation,a combination of the above, etc.

It is noted that, depending on application, the same image can be usedfor a training sample or for ground truth data. By way of non-limitingexample, a CAD-based image can be used as a training sample forsegmentation applications and as ground truth data for regressionapplications. In accordance with certain embodiments of the currentlypresented subject matter, ground truth data can vary by application.Non-limiting examples of application-specific ground truth data areillustrated in Table 2.

TABLE 2 Application-specific examples of ground truth data ApplicationNon-limiting example of ground truth data Classification True class ofeach example Regression The actual image that should be reconstructedSegmentation Segmented images (pixel values are the indices of thesegments) Defect Bounding box or mask, if defects exist detectionRegistration Registration parameters (for parametric modules) or actualshift at each pixel for optical flow

Generating (403) training set of images can include augmenting (411) atleast part of the first training samples and including the augmentedtraining samples in the generated training set, wherein a given firsttraining sample can yield one or more augmented training samples. Anaugmented training sample is derived from a first training sample byaugmenting one or more images in the first training sample. Augmentationof an image from a first training sample can be provided by variousimage processing techniques including adding noise, blurring, geometrictransformation (e.g. rotating, stretching, simulating different angles,cropping, scaling, etc.) tone mapping, changing vector information ofone or more pixels of the image (e.g. adding and/or modifyingperspectives or channels of acquisition, etc.), etc. Alternatively oradditionally, an image from the first training sample can be augmentedusing synthetic data (e.g. defect-related data, simulated connectors orother objects, implants from other images, etc.). By way of non-limitedexample, available images (and/or parameters) of known defect types canbe used to plant a new defect in an image, amplify a defectiveness of apre-existing defect in the image, remove a defect from the image,disguise a defect in the image (making it harder to spot), etc. Yetalternatively or additionally, a captured image from a first trainingsample can be augmented using segmentation, defect contour extractionand/or height map calculation, and/or can be obtained by processingtogether with corresponding CAD-based image.

Augmentation techniques can be applied to the image(s) of the firsttraining sample in an application-independent manner. Alternatively,augmented training samples can be derived in an application-specificmanner, wherein at least a substantial part of respective augmentedimages is obtained by technique(s) preferable to a specific application.Non-limiting examples of application-specific preferable augmentationtechniques are illustrated in Table 3.

TABLE 3 Application-specific examples of preferable augmentationtechniques Non-limiting example of preferable augmentation Applicationtechniques Classification Geometric transformation, tone mapping,implanting synthetic defects, modification of defect tones RegressionAdding noise, blurring, tone mapping Segmentation Adding noise,blurring, tone mapping, synthetic images Defect detection Adding noise,blurring, tone mapping, implanting synthetic defects, modification ofdefect tones Registration Geometric transformation, tone mapping

As a first training sample can yield several augmented training samples,the number of training samples in the training set can be substantiallylarger than a number of first samples. For example, the set of firsttraining samples can include between 100 and 50,000 training samples,while the generated training set can include at least 100,000 trainingsamples. It is noted that capturing images is a slow—and possibly alsocostly—process. Generating a sufficient amount of training samples inthe training set by augmenting the captured images in the first trainingsamples enables reduction of time and/or cost.

Generating (403) training set of images further includes obtaining (412)augmented ground truth data with regard to the augmented trainingsamples and associating (413) the augmented training samples and theaugmented ground truth data. The generated training set can be stored inthe memory of PMB 104 and can comprise application-specific firsttraining samples associated with application-specific ground truth dataand, optionally, augmented training samples associated with augmentedground truth data.

Likewise for first ground truth data, augmented ground truth data can beprovided by a person analyzing the images, with or without the aid of acomputer system. Alternatively or additionally, augmented ground truthdata can be generated by FPEI system by processing the first groundtruth data in correspondence with provided augmentation of the images inrespective first training samples when deriving the augmented trainingsamples.

It is noted that augmenting the first training samples and including theaugmented training samples and augmented ground truth data into thetraining set is optional. In certain embodiments of the disclosure thetraining set can include only first training samples associated withrespective ground truth data.

It is further noted that the training samples can include imagesobtained from examination modalities that are not available duringruntime, such images being used for tuning DNN's training-relatedparameters. By way of non-limiting example, a training sample for defectdetection or classification applications can, in addition tolow-resolution image corresponding to low-resolution FP images to beused during runtime, include corresponding high-resolution SEM imagesavailable only at setup step. Likewise, a training sample forsegmentation or registration application can include, in addition,CAD-based images available only at setup step.

Upon obtaining the application-specific training set, FPEI system usesDNN 112 to iteratively process the training set and to provide (404)application-specific set of training-based parameters and thereby toobtain application-specific trained DNN. Obtained training-basedparameters correspond to application-specific cost functions.Non-limiting examples of application-specific cost functions areillustrated in Table 4. Optionally, processing the training set usingDNN can include pre-process operations by pre-DNN module 113 (e.g.selecting input channels, resizing/cropping, etc.) and post-processoperations by post-DNN module 115 (e.g. executing spatial pyramidpooling, multi-scale pooling, Fisher Vectors, etc.). In addition toapplication-specific optimized weights, training-based parameters caninclude optimized application-specific thresholds, application-specificpre-processing parameters and application-specific post-processingparameters.

TABLE 4 Application-specific examples of cost functions usable fortraining Non-limiting example of cost functions usable for Applicationtraining Classification Classification error (most commonly used is“softmax regression”) Regression Maximal absolute error, MSE, error at agiven percentile Segmentation Segmentation accuracy (measure of correctpixels vs. wrong) Defect Detection accuracy + penalty for misdetectionand over- detection detections Registration Maximal error (across allpixels), relative deviation from model parameters

Thus, in accordance with certain embodiments of the presently disclosedsubject matter, DNN training process bridges betweenapplication-specific training samples and respective ground truth data,thereby enabling further processing of FP images with no need for, beingtypically unfeasible, acquiring ground truth data during runtime.

It is noted that the illustrated training process can be cyclic, and canbe repeated several times until the DNN is sufficiently trained. Theprocess can start from an initially generated training set, while a userprovides a feedback for the results reached by the DNN based on theinitial training set. The provided feedback can include, for example:

-   -   manual re-classification of one or more pixels, regions and/or        defects;    -   prioritization of classes;    -   changes of sensitivity, updates of ground-truth segmentation        and/or manually defining regions of interest (ROIs) for        segmentation applications;    -   re-defining mask/bounding box for defect detection applications;    -   re-selecting failed cases and/or manually registering failures        for registration applications;    -   re-selecting features of interest for regression applications,        etc.

PMB can adjust the next training cycle based on the received feedback.Adjusting can include at least one of: updating the training set (e.g.updating ground truth data and/or augmentation algorithms, obtainingadditional first training samples and/or augmented training samples,etc.), updating cost function, updating pre-DNN and/or post/DNNalgorithms, etc. Optionally, some of the training cycles can be providednot to the entire DNN 112, but rather to pre-DNN module 113, post-DNNmodule 115 or to one or more higher layers of DNN module 114.

FIG. 4 illustrates training a deep neural network directly on datarelated to fabricated specimens. It is noted that the teachings of thepresently disclosed subject matter are, likewise, applicable to DNNscoarsely trained on a different data set, possibly irrelevant to thefabricated specimens, and further finely trained for specificexamination-related application (e.g. with the help of transfer learningtechnique). Likewise, DNN can be coarsely trained (pre-trained) usingother techniques known in the art.

Non-limiting examples of implementing the detailed above technique ofobtaining examination-related data using the application-specifictrained DNN network are further detailed with reference to FIGS. 5-9.The processes illustrated with reference to FIGS. 5-9 comprise a setupstep of application-specific training of DNN, and runtime step of usingthe trained DNN for specific application. PMB further uses the trainedDNN to processes together one or more FP images and, to obtain, thereby,application-specific examination-related data. The one or more FP imagesconstitute a fabrication process sample (FP sample). Depending onapplication, a FP sample can be a single image or a group of imagesobtained by the same or by different examination modalities. It is notedthat training samples shall correspond to FP samples to be used forrespective application. For a given application, each first trainingsample shall comprise at least the same number of images obtained by thesame examination modalities and being in the same relationship as theimages in a respective FP sample. Optionally, training samples canfurther comprise additional images obtained by additional examinationmodalities being, typically, unavailable during runtime. Referring toFIGS. 5a and 5b , there are illustrated non-limiting examples ofimplementing the detailed above technique of obtainingexamination-related data for classifying defects in a specimen. Theillustrated method of operating the FPEI system can be usable, forexample, for automatic defect classification (ADC).

The process comprises a setup step 510 of classification-specifictraining of DNN, and runtime step 520 of using the trained DNN fordefect classification.

During the setup 510 (common for FIGS. 5a and 5b ), upon obtaining theset of first training samples (501) and respective ground truth data(502), PMB 104 generates (503) a classification training set and usesthe generated classification training set to obtain (504) the trainedDNN characterized by classification-related training parameters.Generating the classification training set can include augmenting thefirst training samples and the ground truth data and including theaugmented training samples and augmented ground truth data into thetraining set.

Each of the first training samples can comprise a singlepreviously-captured high resolution image of a defect. Optionally, atleast part of such single images can be images of known defect types;such images can be available from one or more 3 ^(rd) party databases.Optionally, a single image in a first training sample can be an“intermediate” image previously derived from a defect image (e.g. bysegmentation, defect contour extraction, height map calculation, etc.)and stored in a data repository (e.g. data repository 109). Optionally,each of the first training samples can further comprise images fromadditional modalities as, for example, reference die images, CAD-basedimages, height map, defect mask, images obtained from differentperspectives, etc.

An augmented training sample can be obtained by augmenting a firsttraining sample (e.g. by geometrical warping, planting a new defect inan image, amplifying a defectiveness of a pre-existing defect in theimage, removing a defect from the image, disguising a defect in theimage, etc.)

The obtained ground truth data associated with the first trainingsamples is informative of classes (e.g. particles, pattern deformation,bridges, etc.) and/or of class distribution (e.g. probability ofbelonging to each of the classes) of defects presented in the firsttraining samples Likewise, the augmented ground truth data isinformative of classes/class distribution of defects in the augmentedtraining samples.

Thus, the generated classification training set can include trainingsamples with high-resolution captured defect images, associated groundtruth data informative of classes and/or class distribution of defectsin the captured images and, optionally, the augmented training samplesand augmented ground truth data informative of classes and/or classdistribution of defects in the augmented training samples.

Upon generating (503) the classification training set, PMB trains (504)the DNN to extract classification-related features and to provideclassification-related attributes enabling minimal classification error.The training process yields the trained DNN with classification-relatedtraining parameters.

During runtime 520, PMB uses the classification-specific trained DNN toprocess (505) a FP sample comprising a captured high-resolution FPdefect image and to obtain (506) automatic classification-relatedattributes. Optionally, an FP sample can further comprise, incorrespondence with training samples, reference die images, CAD-basedimages, height map, defect mask, etc., these FP images to be processedby DNN together with the high resolution FP defect image. PMB canfurther obtain (507) engineered attributes (e.g. defect size,orientation, background segment, etc.) related to the defect to beclassified. Engineered attributed can be generated by PMB in accordancewith predefined instructions stored in PMB.

In the process illustrated in FIG. 5a , FPEI system exports (508) theclassification-related attributes obtained by DNN and, optionally, theengineered attributes to an external classifier, and further exports theengineered attributes to an external classification system. Obtainingclassification results (509) includes processing by an externalclassification system the results it receives from the externalclassifier (which can be, optionally, a part of the externalclassification system) together with engineered attributes.

In the process illustrated in FIG. 5b , FPEI system uses theclassification-related attributes obtained by DNN and, optionally, theengineered attributes (optionally obtained when processing FP image(s))to generate (508-1) intermediate classification results. FPEI systemfurther exports (508-1) the intermediate classification results and theengineered attributes to an external classification system. The externalclassification system processes (509-1) the received data and yields theclassified defect(s). Optionally, operation 508-1 can be omitted, andFPEI can use classification-related attributes obtained by DNN and theengineered attributes to yield the classified defects with noinvolvement of the external classification system.

Thus, as illustrated, the classification application can be implementedin different ways. By way of non-limiting example, theclassification-specific trained DNN can classify a defect presented inFP image based either on a combination of DNN classification-relatedattributes and engineered attributes it obtains or base, merely, on theDNN obtained classification-related attributes. Alternatively,classification-specific trained DNN can enable classification of suchdefect by providing classification-related attributes (and, optionally,engineered attributes) to an external classification system.

Referring to FIG. 6, there is illustrated a non-limiting example ofimplementing the technique detailed with reference to FIGS. 1-4 forsegmentation of an FP image. Unless specifically stated otherwise, theterm “ segmentation” used in this specification should be expansivelyconstrued to cover any process of partitioning the image into meaningfulparts (for example, background and foreground or defect and non-defect,etc.) whilst providing per-pixel values. By way of non-limiting example,it can be usable for ADC when constructing attributes (e.g. for definingif the defect is on the main pattern, on the background, or both), forADR for applying segment-specific detection thresholds on each segment,etc.

The process comprises a setup step 610 of segmentation-specific trainingof DNN and runtime step 620 of using the trained DNN for imagesegmentation.

During the setup 610, upon obtaining the set of first training samples(601) and ground truth data (602), PMB 104 generates (603) segmentationtraining set and uses the generated segmentation training set to obtain(604) the trained DNN characterized by segmentation-related trainingparameters. Optionally, generating segmentation training set can includeaugmenting the first training samples and obtaining augmented groundtruth data associated therewith.

The first training samples can include previously-capturedlow-resolution images and/or high-resolution images and, optionally,CAD-based images. Optionally, the training set can further compriseaugmented training samples (e.g. by adding noise, blurring, tonemapping, etc.). The obtained ground truth data is informative ofsegments-related data associated with respective training samples. Byway of non-limiting example, the segments-related data associated with agiven training sample can be informative of per-pixel segmentation;per-pixel labels; CAD polygons; CAD models; ROIs, etc. Likewise, theaugmented ground truth data is informative of segments-relatedinformation with regard to respective augmented training samples.

Upon generating (603) the segmentation training set, PMB trains (604)the DNN to provide required pixel values (e.g. a grayscale image inwhich different color value for each pixels represent different segmentson the image; representation of segments as, for example, edges orvertices of each segment, etc.) with minimal error. The training processyields the trained DNN with segmentation-related training parameters.

During runtime 620, PMB uses the trained DNN to process (605) a FPsample comprising a captured FP image to be segmented (and, optionally,additional FP images in correspondence with training samples) in orderto provide (606) the segmentation thereof. The obtained segments-relateddata can be informative of per-pixel segmentation, per-pixel labels, CADpolygons, CAD models, ROIs in the FP image, etc.

Referring to FIG. 7, there is illustrated a non-limiting example ofimplementing the technique detailed with reference to FIGS. 1-4 forobtaining information related to defect detection, for example filteringpotential defects in the image as defects or non-defects, and/orproviding position, size, bounding box, borders and/or mask, etc. of theidentified defects.

The process comprises a setup step 710 of detection-specific training ofDNN and runtime step 720 of using the trained DNN for obtaininginformation related to defect detection.

During the setup 710, upon obtaining the set of first training samples(701) and ground truth data (702), PMB 104 generates (703) detectiontraining set and uses the generated detection training set to obtain(704) the trained DNN characterized by detection-related trainingparameters. Generating the detection training set can include augmentingthe first training samples and obtaining augmented ground truth data.

The training samples can include a single image informative of suspectedlocations (e.g. area images with ROI images centered on defectcandidates, low resolution area images, etc.) or a group of images (e.g.the image informative of suspected locations in combination with areference image, images of the same area of interest obtained indifferent perspectives, etc.).

The ground truth values for each training sample of the training set caninclude a list of candidate defects, a truth value (e.g.defect/not-defect, true/false) for each of the candidate defects;localization for each true defect (e.g. defect bounding box or mask),etc. Upon generating (703) the detection training set, PMB trains (704)the DNN to provide required detection-related information with minimalerror. The training process yields the trained DNN withdetection-related training parameters.

During runtime 720, PMB uses the trained DNN to process (705) a FPsample comprising a captured FP image (and, optionally, additional FPimages in correspondence with training samples)to obtain datainformative of true defects (e.g. determine true defects (if any), markthe true defects (e.g. by bounding boxes or by providing a binary imagein which only pixels belonging to the defect get a “1” value andnon-defected pixels get a “0” value), obtain shape-related information,etc.).

Referring to FIG. 8, there is illustrated a non-limiting example ofimplementing the technique detailed with reference to FIGS. 1-4 forregistration between images received from the same or from differentexamination modalities.

The process comprises a setup step 810 of registration-specific trainingof DNN and runtime step 820 of using the trained DNN for obtaininginformation related to registration.

During the setup 810, upon obtaining the set of first training samples(801) and ground truth data (802), PMB 104 generates (803) registrationtraining set and uses the generated registration training set to obtain(804) the trained DNN characterized by registration-related trainingparameters.

The training set includes training samples each comprising at least apair of images, wherein one image in each pair is registerable withregard to another image in the pair. The images in a given trainingsample can be from the same or from different examination modalities.

The ground truth data for each given training sample can includeregistration-related data with regard to the given training sample (e.g.parametric model representation and parameters thereof (e.g. affine,rotations, translations, reflections, and their combinations, etc.).Optionally, ground truth data can also include per-pixel transformationmap (e.g. per-pixel shifts to be provided locally). Alternatively,during training, per-pixel transformation map can be generated bypre-DNN module based on available ground truth data.

Generating a registration training set can include augmenting the firsttraining samples and obtaining augmented ground truth data with regardto the augmented training samples.

Upon generating (803) the registration training set, PMB trains (804)the DNN to provide required registration-related information withminimal error. The training process yields the trained DNN withregistration-related training parameters.

During runtime 820, PMB uses the trained DNN to process (805) a FPsample comprising a pair of FP image (and, optionally, additional FPimages in correspondence with training samples) to be registered one toanother and obtains registration-related parameters of the pair (e.g.per-pixel transformation map and/or other transformation modelparameters). The registered images can be later compared (e.g.die-to-die, cell-to-cell, die-to-CAD) for detecting discrepanciesindicative of potential defects, improving defect localization in CADcoordinates, etc.

Referring to FIG. 9, there is illustrated a non-limiting example ofimplementing the technique detailed with reference to FIGS. 1-4 forregression application enabling reconstructing an image of specimen (orpart thereof) from one or more specimen images from differentexamination modality(s). By way of non-limiting example, suchapplications include simulating SEM images from CAD data, simulating SEMimage from optical data, etc.

The process comprises a setup step 910 of regression-specific trainingof DNN and runtime step 920 of using the trained DNN for obtainingregression-related information.

During the setup 910, upon obtaining the set of first training samples(901) and ground truth data (902), PMB 104 generates (903) aregression-specific training set and uses the generatedregression-specific training set to obtain (904) the trained DNNcharacterized by regression-specific training parameters.

The regression-specific training set includes first training samples,each comprising one or more images previously obtained by a firstexamination modality or modalities. For each given first trainingsample, ground truth data include one or more images of thecorresponding specimen (or part thereof) obtained by another examinationmodality and associated with the given first training sample. It isnoted that the ground truth images obtained by the second examinationmodality can be images actually captured by the second examinationmodality or reconstructed image corresponding to the respective imagesin the first training samples.

Generating regression-specific training set can include augmenting thefirst training samples and obtaining augmented ground truth data bycorresponding augmenting ground truth images associated with the firsttraining samples.

Upon generating (903) the regression-specific training set, PMB trains(904) the DNN to provide required regression-specific information withminimal error. The training process yields the trained DNN withregression-specific training parameters.

During runtime 920, PMB uses the trained DNN to process (905) a FPsample comprising a FP image (and, optionally, additional FP images incorrespondence with training samples) from one examination modality soto obtain data usable for reconstructing the FP image in correspondencewith another examination modality. The reconstructed image can befurther compared to the corresponding image of the second examinationmodality (e.g. for detecting discrepancies indicative of potentialdefects, registration, changes optical modes, etc.).

It is noted that setup steps illustrated with reference to FIGS. 5-9 canbe cyclic, and respective training can be repeated several times(optionally, with an updated training set) until the DNN is sufficientlytrained.

It is noted that an examination flow can include two or moreapplications illustrated with reference to FIGS. 5-9, each applicationwith its own application-specific trained DNN. The obtainedapplication-specific defect-related information can be further fed intoa bigger system (e.g. ADC classifiers or ADR detector).

It is to be understood that the disclosure is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings. The disclosure is capable of otherembodiments and of being practiced and carried out in various ways.Hence, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting. As such, those skilled in the art will appreciatethat the conception upon which this disclosure is based may readily beutilized as a basis for designing other structures, methods, and systemsfor carrying out the several purposes of the presently disclosed subjectmatter.

It will also be understood that the system according to the disclosuremay be, at least partly, implemented on a suitably programmed computer.Likewise, the disclosure contemplates a computer program being readableby a computer for executing the method of the disclosure. The disclosurefurther contemplates a non-transitory computer-readable memory tangiblyembodying a program of instructions executable by the computer forexecuting the method of the disclosure.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of thedisclosure as hereinbefore described without departing from its scope,defined in and by the appended claims.

1. A method of examination of a semiconductor specimen, the methodcomprising: upon obtaining by a computer a Deep Neural Network (DNN)trained for a given examination-related application within asemiconductor fabrication process, processing together one or morefabrication process (FP) images using the obtained trained DNN, whereinthe DNN is trained using a training set comprising ground truth dataspecific for the given application; and obtaining by the computerexamination-related data specific for the given application andcharacterizing at least one of the processed one or more FP images. 2.The method of claim 1, wherein the training set comprises a plurality offirst training samples and a plurality of augmented training samplesobtained by augmenting at least part of the first training samples. 3.The method of claim 2, wherein the training set comprises ground truthdata associated with the first training samples and augmented groundtruth data associated with the augmented training samples.
 4. The methodof claim 2, wherein at least substantial part of augmented trainingsamples is obtained by one or more augmenting techniques preferable tothe given application.
 5. The method of claim 1, wherein the trainingset comprises a plurality of first training samples, each first trainingsample comprises at least one image obtained by an examination modalityselected from the group consisting of: optical inspection;multi-perspective optical inspection, low-resolution inspection byelectronic microscope, high-resolution inspection by electronicmicroscope, image generation based on design data, image generation byaltering a captured image.
 6. The method of claim 1, wherein the one ormore FP images constitute a FP sample and wherein the training setcomprises a plurality of first training samples each comprising imagesobtained from the same modalities as the one or more FP images.
 7. Themethod of claim 6, wherein each first training sample further comprisesat least one image obtained by an examination modality other than one ormore examination modalities used for obtaining the one or more FPimages.
 8. The method of claim 1, wherein the given examination-relatedapplication is related to a certain production layer, wherein thetraining set comprises ground truth data specific for said certainproduction layer and wherein the examination-related data is specificfor said certain production layer.
 9. The method of claim 1, wherein thegiven examination-related application is related to a certain virtuallayer consisting of one or more production layers with a similar nature,wherein the training set comprises ground truth data specific for saidcertain virtual layer and wherein the examination-related data isspecific for said certain virtual layer.
 10. The method of claim 1,wherein the examination comprises at least a first examination-relatedapplication and a second examination-related application, the methodfurther comprising using for the first application a DNN trained using atraining set comprising ground truth data specific for the firstapplication and using for the second application a DNN trained using atraining set comprising ground truth data specific for the secondapplication.
 11. The method of claim 3, wherein the givenexamination-related application is classifying at least one defectpresented by the at least one FP image, and wherein the ground truthdata is informative of classes and/or of class distribution of defectspresented in the first training samples and augmented ground truth datais informative of classes and/or of class distribution of defectspresented in the augmented training samples.
 12. The method of claim 2,wherein the given examination-related application is classifying atleast one defect presented by the at least one FP image, and whereinaugmenting at least part of the first training samples is provided by atleast one technique selected from the group consisting of geometricalwarping, planting a new defect in an image, amplifying a defectivenessof a pre-existing defect in an image, removing a pre-existing defectfrom an image and disguising a defect in an image.
 13. The method ofclaim 1, wherein the given examination-related application is segmentingthe at least one FP image which is selected from the group comprising ahigh-resolution image of the specimen, a low-resolution image of thespecimen and a design data-based image of the specimen; and wherein theexamination-related data is informative of per-pixelsegmentation-related values of the at least one FP image.
 14. The methodof claim 1, wherein the given examination-related application isdetecting defects in the specimen and wherein the examination-relateddata is informative of true defects presented in the at least one FPimage.
 15. The method of claim 1, wherein the given examination-relatedapplication is registration between at least two FP images, wherein thetraining set comprises a plurality of training samples each comprisingat least a pair of images registerable one with regard to another, andwherein the examination-related data is informative ofregistration-related data with regard to said at least two FP images.16. The method of claim 1, wherein the given examination-relatedapplication is regression application enabling reconstructing the atleast one FP image in correspondence with different examinationmodality, wherein the training set comprises a plurality of trainingsamples with images obtained by first examination modalities, each saidtraining sample associated with ground truth data comprising one or morecorresponding images obtained by one or more another examinationmodalities.
 17. A non-transitory computer readable medium comprisinginstructions that, when executed by a computer, cause the computer toperform a method of examination of a semiconductor specimen, the methodcomprising: upon obtaining by the computer a Deep Neural Network (DNN)trained for a given examination-related application within asemiconductor fabrication process, processing together one or morefabrication process (FP) images using the obtained trained DNN, whereinthe DNN is trained using a training set comprising ground truth dataspecific for the given application; and obtaining by the computerexamination-related data specific for the given application andcharacterizing at least one of the processed one or more FP images. 18.The non-transitory computer readable medium of claim 17, wherein thetraining set comprises a plurality of first training samples, aplurality of augmented training samples obtained by augmenting at leastpart of the first training samples, ground truth data associated withthe first training samples and augmented ground truth data associatedwith the augmented training samples.
 19. The non-transitory computerreadable medium of claim 17, wherein the given examination-relatedapplication is selected from the group consisting of classifying atleast one defect presented by the at least one FP image, segmenting theat least one FP image, detecting defects in the specimen presented bythe at least one FP image, registering between at least two FP images,and regression application enabling reconstructing the at least one FPimage in correspondence with different examination modality.
 20. Asystem usable for examination of a semiconductor specimen, the systemcomprising a processing and memory block (PMB) operatively connected toan input interface and an output interface, wherein: the input interfaceis configured to receive one or more fabrication process (FP) images;the PMB is configured to obtain a Deep Neural Network (DNN) trained fora given examination-related application within a semiconductorfabrication process and to process together the one or more FP imagesusing the trained DNN to obtain examination-related data specific forthe given application and characterizing at least one of the processedone or more FP images, wherein the DNN is trained using a training setcomprising ground truth data specific for the given application; theoutput interface is configured to output the obtainedexamination-related data.